Development of a multi-agent recommender system for intelligent shopping assistants
Subject Areas : Multimedia Processing, Communications Systems, Intelligent Systems
Ramazan Teimouri Yansari
1
,
Mojtaba Ajoudani
2
1 - Assistant Professor, Department of Computer Engineering, Bandar Gaz Branch, Islamic Azad University, Bandar Gaz, Iran
2 - Assistant Professor, Department of Electrical Engineering, Bandar Gaz Branch, Islamic Azad University, Bandar Gaz, Iran
Keywords: Intelligent agents, Intelligent shopping assistants, Machine learning, Multi agent systems (MAS), Recommender systems.,
Abstract :
Introduction: Due to the increasing volume and services available on the web, tools such as recommender systems in websites and applications that can help users find information and services of interest can be provided. For this reason, suitable guidance and suggestions for users in different choices, according to the user's priorities in different areas of a specific position, have been provided.
Method: Recommender systems are information systems that assist in the decision-making process by modeling the behavior of users in operational environments in ranking, comparing, selecting and choose items by users, narrowing the information search through high-quality and accurate recommendations. In this research, a multi-agent recommender system is proposed as an intelligent shopping assistant in the process of buying suitable offers. The proposed model is used to analyze the sales data set of a UK-based store containing 1,067,371 records of online sales data.
Results: By simulating the proposed model, the results of applying the model to the relevant data were analyzed. The proposed model in this research was simulated in MATLAB software version 2022 and the results of applying the proposed model on the data related to the sale of an online shopping were analyzed. According to the results, in this evaluation, the accuracy of the proposed model was 91.5% on average, compared to the neural network model, it was 86.41%, compared to the KNN model, 78.32%, compared to the SOM Ensembles model, 74.38%, compared to the Global Top-N model, 69.78%, compared to the Weighted item-based model, 72.31%, and compared to The Naïve Bayesian model has an accuracy of 59.68%, a higher accuracy in the right suggestion to users.
Discussion: In this research, while studying recommender systems, the challenges in this field were examined and multi-agent systems were used to provide suggestions and recommendations with high accuracy and quality in ranking, comparison, selection and preferences of users' items in the decision-making process in operational environments. By combining multi-agent systems, multi-agent recommender systems were proposed that can provide suitable recommendations as a purchasing assistant in the purchasing process. The results of applying the proposed model on the data related to the purchase history of the customers of an online shopping showed that the proposed model has a good efficiency in evaluating the parameters used in comparison with the common methods in this property field.
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